Cisco
predicts that annual global IP traffic will reach 3.3 ZB per year by 2021 and that the number of devices connected to IP networks will be more than three times the global population by 2021, while Gartner predicts
$2.5M per minute in IoT spending and 1M new IoT devices will be sold
every hour by 2021. It’s testament to the speed with which digital
connectivity is changing the lives of people all over the world.

Data has also evolved dramatically in recent years, in type, volume,
and velocity -- with its rapid evolution attributed to the widespread
digitization of business processes globally. Data has become the new
business currency and its further rapid increase will be key to the
transformation and growth of enterprises globally, and the advancement
of employees, "the digital natives."

The Cisco Global Cloud Index points
to the cloud as the top driver as exponential data center growth with
cloud center traffic quadrupling in the next five years. Data generated
by IoT applications (such as connected homes, smart cities and
healthcare) will be 600ZB (zettabytes) per year by 2020, 39 times higher
than current data center traffic which is 15.3ZB.

Big data therefore has a far-reaching impact and meaning. But how do
we understand it and its benefits, along with analytics on the journey
to digital transformation? Understanding the value of data is key to the
successful implementation of operational strategies that facilitate
agile and effective business growth.

Big data means better business Data is an enabler of future strategies and immediate change, thanks
to the power of predictive analytics and advanced data science.
Correctly harnessing data can help to achieve better, fact-based
decision-making and improve the overall customer experience. By using
new big data technologies, organizations can answer questions in seconds
rather than days, and in days rather than months. This acceleration
allows businesses to enable the type of quick reactions to key business
questions and challenges that can build competitive advantage and
improve performance, and provide answers for complex problems or
questions that have resisted analysis...

Keep learning -- skills are everythingProficiency with data mining and visualization tools ranks as one of the most important skills in determining project success.

All organizations need to consistently develop new data mining skills
to fully realize the business potential. A key trend in big data is
machine learning. Big data experts who can harness machine learning
technology to build and train predictive analytic apps such as
classification, recommendation, and personalization systems are in high
demand.

Statistical and quantitative analysis, which aims to understand or
predict behavior or events through the use of mathematical measurements
and calculations, statistical modeling and research, is also imperative
to accomplishment. Other key data mining techniques that are employed industry wide include:

Association is one of the best-known data mining techniques. With
association, a pattern is discovered based on a relationship between
items in the same transaction

Classification is a classic data mining technique based on machine learning

Clustering is a data mining technique that makes a meaningful or
useful cluster of objects which have similar characteristics using the
automatic technique

Prediction is one of a data mining techniques that discovers the
relationship between independent variables and relationship between
dependent and independent variables

Sequential patterns analysis seeks to discover or identify similar
patterns, regular events or trends in transaction data over a business
period

Decision tree technique, the root of the decision tree is a simple question or condition that has multiple answers

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About Me

Hello, my name is Helge Scherlund and I am the Education Editor and Online Educator of this personal weblog and the founder of eLearning • Computer-Mediated Communication Center.
I have an education in the teaching adults and adult learning from Roskilde University, with Computer-Mediated Communication (CMC) and Human Resource Development (HRD) as specially studied subjects. I am the author of several articles and publications about the use of decision support tools, e-learning and computer-mediated communication. I am a member of The Danish Mathematical Society (DMF), The Danish Society for Theoretical Statistics (DSTS) and an individual member of the European Mathematical Society (EMS). Note: Comments published here are purely my own and do not reflect those of my current or future employers or other organizations.